

'''simple docstring'''






import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union

# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
    CONFIG_MAPPING_NAMES,
    AutoConfig,
    model_type_to_module_name,
    replace_list_option_in_docstrings,
)


__SCREAMING_SNAKE_CASE     =						logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE     =						OrderedDict(
    [
        ('audio-spectrogram-transformer', 'ASTFeatureExtractor'),
        ('beit', 'BeitFeatureExtractor'),
        ('chinese_clip', 'ChineseCLIPFeatureExtractor'),
        ('clap', 'ClapFeatureExtractor'),
        ('clip', 'CLIPFeatureExtractor'),
        ('clipseg', 'ViTFeatureExtractor'),
        ('conditional_detr', 'ConditionalDetrFeatureExtractor'),
        ('convnext', 'ConvNextFeatureExtractor'),
        ('cvt', 'ConvNextFeatureExtractor'),
        ('data2vec-audio', 'Wav2Vec2FeatureExtractor'),
        ('data2vec-vision', 'BeitFeatureExtractor'),
        ('deformable_detr', 'DeformableDetrFeatureExtractor'),
        ('deit', 'DeiTFeatureExtractor'),
        ('detr', 'DetrFeatureExtractor'),
        ('dinat', 'ViTFeatureExtractor'),
        ('donut-swin', 'DonutFeatureExtractor'),
        ('dpt', 'DPTFeatureExtractor'),
        ('encodec', 'EncodecFeatureExtractor'),
        ('flava', 'FlavaFeatureExtractor'),
        ('glpn', 'GLPNFeatureExtractor'),
        ('groupvit', 'CLIPFeatureExtractor'),
        ('hubert', 'Wav2Vec2FeatureExtractor'),
        ('imagegpt', 'ImageGPTFeatureExtractor'),
        ('layoutlmv2', 'LayoutLMv2FeatureExtractor'),
        ('layoutlmv3', 'LayoutLMv3FeatureExtractor'),
        ('levit', 'LevitFeatureExtractor'),
        ('maskformer', 'MaskFormerFeatureExtractor'),
        ('mctct', 'MCTCTFeatureExtractor'),
        ('mobilenet_v1', 'MobileNetV1FeatureExtractor'),
        ('mobilenet_v2', 'MobileNetV2FeatureExtractor'),
        ('mobilevit', 'MobileViTFeatureExtractor'),
        ('nat', 'ViTFeatureExtractor'),
        ('owlvit', 'OwlViTFeatureExtractor'),
        ('perceiver', 'PerceiverFeatureExtractor'),
        ('poolformer', 'PoolFormerFeatureExtractor'),
        ('regnet', 'ConvNextFeatureExtractor'),
        ('resnet', 'ConvNextFeatureExtractor'),
        ('segformer', 'SegformerFeatureExtractor'),
        ('sew', 'Wav2Vec2FeatureExtractor'),
        ('sew-d', 'Wav2Vec2FeatureExtractor'),
        ('speech_to_text', 'Speech2TextFeatureExtractor'),
        ('speecht5', 'SpeechT5FeatureExtractor'),
        ('swiftformer', 'ViTFeatureExtractor'),
        ('swin', 'ViTFeatureExtractor'),
        ('swinv2', 'ViTFeatureExtractor'),
        ('table-transformer', 'DetrFeatureExtractor'),
        ('timesformer', 'VideoMAEFeatureExtractor'),
        ('tvlt', 'TvltFeatureExtractor'),
        ('unispeech', 'Wav2Vec2FeatureExtractor'),
        ('unispeech-sat', 'Wav2Vec2FeatureExtractor'),
        ('van', 'ConvNextFeatureExtractor'),
        ('videomae', 'VideoMAEFeatureExtractor'),
        ('vilt', 'ViltFeatureExtractor'),
        ('vit', 'ViTFeatureExtractor'),
        ('vit_mae', 'ViTFeatureExtractor'),
        ('vit_msn', 'ViTFeatureExtractor'),
        ('wav2vec2', 'Wav2Vec2FeatureExtractor'),
        ('wav2vec2-conformer', 'Wav2Vec2FeatureExtractor'),
        ('wavlm', 'Wav2Vec2FeatureExtractor'),
        ('whisper', 'WhisperFeatureExtractor'),
        ('xclip', 'CLIPFeatureExtractor'),
        ('yolos', 'YolosFeatureExtractor'),
    ]
)

__SCREAMING_SNAKE_CASE     =						_LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)



def 			__a		(       lowerCAmelCase__ :    Tuple      ):



     for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
          if class_name in extractors:
               a__   :      List[Any]     =						model_type_to_module_name(lowerCAmelCase__      )

               a__   :      Optional[Any]     =						importlib.import_module(F'.{module_name}'     ,  '''transformers.models'''      )
               try:
                    return getattr(lowerCAmelCase__     ,  lowerCAmelCase__      )
               except AttributeError:
                    continue

     for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
          if getattr(lowerCAmelCase__     ,  '''__name__'''     ,  lowerCAmelCase__      ) == class_name:
               return extractor

    # We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
    # init and we return the proper dummy to get an appropriate error message.
     a__   :      Tuple     =						importlib.import_module('''transformers'''      )
     if hasattr(lowerCAmelCase__     ,  lowerCAmelCase__      ):
          return getattr(lowerCAmelCase__     ,  lowerCAmelCase__      )

     return None



def 			__a		(       lowerCAmelCase__ :    Any     ,  lowerCAmelCase__ :    Tuple = None     ,  lowerCAmelCase__ :    str = False     ,  lowerCAmelCase__ :    Any = False     ,  lowerCAmelCase__ :    str = None     ,  lowerCAmelCase__ :    int = None     ,  lowerCAmelCase__ :    List[str] = None     ,  lowerCAmelCase__ :    Any = False     ,  **lowerCAmelCase__ :    Union[str, Any]     ,  ):
     a__   :      List[str]     =						get_file_from_repo(
         lowerCAmelCase__     ,  lowerCAmelCase__     ,  cache_dir=lowerCAmelCase__     ,  force_download=lowerCAmelCase__     ,  resume_download=lowerCAmelCase__     ,  proxies=lowerCAmelCase__     ,  use_auth_token=lowerCAmelCase__     ,  revision=lowerCAmelCase__     ,  local_files_only=lowerCAmelCase__     ,  )
     if resolved_config_file is None:
          logger.info(
              '''Could not locate the feature extractor configuration file, will try to use the model config instead.'''      )
          return {}

     with open(lowerCAmelCase__     ,  encoding='''utf-8'''      ) as reader:
          return json.load(lowerCAmelCase__      )






class 			lowerCAmelCase__     :




     """simple docstring"""





     def __init__(		self						:	Optional[Any]	)       ->			Optional[Any]:
          '''simple docstring'''




          raise EnvironmentError(
              '''AutoFeatureExtractor is designed to be instantiated '''
              '''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.'''	)







     @classmethod
     @replace_list_option_in_docstrings(lowerCAmelCase__	)
     def 			__lowerCAmelCase	(		cls						:	Optional[int]      ,						A__						:	Optional[Any]      ,						**A__						:	Optional[Any]	)       ->			int:
          '''simple docstring'''




          a__   :      Union[str, Any]     =						kwargs.pop('''config'''      ,						lowerCAmelCase__	)
          a__   :      Dict     =						kwargs.pop('''trust_remote_code'''      ,						lowerCAmelCase__	)
          a__   :      int     =						True

          a__     ,    a__   :      Optional[Any]     =						FeatureExtractionMixin.get_feature_extractor_dict(lowerCAmelCase__      ,						**lowerCAmelCase__	)
          a__   :      Union[str, Any]     =						config_dict.get('''feature_extractor_type'''      ,						lowerCAmelCase__	)
          a__   :      List[str]     =						None
          if "AutoFeatureExtractor" in config_dict.get('''auto_map'''      ,						{}	):
               a__   :      int     =						config_dict['''auto_map''']['''AutoFeatureExtractor''']

          # If we don't find the feature extractor class in the feature extractor config, let's try the model config.
          if feature_extractor_class is None and feature_extractor_auto_map is None:
               if not isinstance(lowerCAmelCase__      ,						lowerCAmelCase__	):
                    a__   :      List[str]     =						AutoConfig.from_pretrained(lowerCAmelCase__      ,						**lowerCAmelCase__	)
               # It could be in `config.feature_extractor_type``
               a__   :      Any     =						getattr(lowerCAmelCase__      ,						'''feature_extractor_type'''      ,						lowerCAmelCase__	)
               if hasattr(lowerCAmelCase__      ,						'''auto_map'''	) and "AutoFeatureExtractor" in config.auto_map:
                    a__   :      List[str]     =						config.auto_map['''AutoFeatureExtractor''']

          if feature_extractor_class is not None:
               a__   :      str     =						feature_extractor_class_from_name(lowerCAmelCase__	)

          a__   :      int     =						feature_extractor_auto_map is not None
          a__   :      Any     =						feature_extractor_class is not None or type(lowerCAmelCase__	) in FEATURE_EXTRACTOR_MAPPING
          a__   :      Tuple     =						resolve_trust_remote_code(
              lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__      ,						lowerCAmelCase__	)

          if has_remote_code and trust_remote_code:
               a__   :      List[Any]     =						get_class_from_dynamic_module(
                   lowerCAmelCase__      ,						lowerCAmelCase__      ,						**lowerCAmelCase__	)
               a__   :      List[Any]     =						kwargs.pop('''code_revision'''      ,						lowerCAmelCase__	)
               if os.path.isdir(lowerCAmelCase__	):
                    feature_extractor_class.register_for_auto_class()
               return feature_extractor_class.from_dict(lowerCAmelCase__      ,						**lowerCAmelCase__	)
          elif feature_extractor_class is not None:
               return feature_extractor_class.from_dict(lowerCAmelCase__      ,						**lowerCAmelCase__	)
          # Last try: we use the FEATURE_EXTRACTOR_MAPPING.
          elif type(lowerCAmelCase__	) in FEATURE_EXTRACTOR_MAPPING:
               a__   :      Union[str, Any]     =						FEATURE_EXTRACTOR_MAPPING[type(lowerCAmelCase__	)]
               return feature_extractor_class.from_dict(lowerCAmelCase__      ,						**lowerCAmelCase__	)

          raise ValueError(
              F'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
              F'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
              F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys()	)}'	)







     @staticmethod
     def 			__lowerCAmelCase	(		A__						:	Tuple      ,						A__						:	Dict	)       ->			Dict:
          '''simple docstring'''




          FEATURE_EXTRACTOR_MAPPING.register(lowerCAmelCase__      ,						lowerCAmelCase__	)

import inspect
from typing import Callable, List, Optional, Union

import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextModel,
    CLIPTokenizer,
    WhisperForConditionalGeneration,
    WhisperProcessor,
)

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.utils import logging


__a    :	Tuple         =    logging.get_logger(__name__)  # pylint: disable=invalid-name




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			def __init__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  )	->   Optional[int]:

							'''simple docstring'''


							super().__init__()

							if safety_checker is None:
											logger.warning(
											    F"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
											    ''' that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered'''
											    ''' results in services or applications open to the public. Both the diffusers team and Hugging Face'''
											    ''' strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling'''
											    ''' it only for use-cases that involve analyzing network behavior or auditing its results. For more'''
											    ''' information, please have a look at https://github.com/huggingface/diffusers/pull/254 .'''		)

							self.register_modules(
							    speech_model=lowerCAmelCase__ ,  speech_processor=lowerCAmelCase__ ,  vae=lowerCAmelCase__ ,  text_encoder=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  unet=lowerCAmelCase__ ,  scheduler=lowerCAmelCase__ ,  feature_extractor=lowerCAmelCase__ ,  )





			def 			_SCREAMING_SNAKE_CASE     (  self ,  lowerCAmelCase__ = "auto"		)	->   Dict:

							'''simple docstring'''


							if slice_size == "auto":
											__lowercase					     =				self.unet.config.attention_head_dim // 2
							self.unet.set_attention_slice(lowerCAmelCase__		)





			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   int:

							'''simple docstring'''


							self.enable_attention_slicing(lowerCAmelCase__		)





			@torch.no_grad()
			def __call__(  self ,  lowerCAmelCase__ ,  lowerCAmelCase__=1_60_00 ,  lowerCAmelCase__ = 5_12 ,  lowerCAmelCase__ = 5_12 ,  lowerCAmelCase__ = 50 ,  lowerCAmelCase__ = 7.5 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = 1 ,  lowerCAmelCase__ = 0.0 ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = "pil" ,  lowerCAmelCase__ = True ,  lowerCAmelCase__ = None ,  lowerCAmelCase__ = 1 ,  **lowerCAmelCase__ ,  )	->   List[str]:

							'''simple docstring'''


							__lowercase					     =				self.speech_processor.feature_extractor(
							    lowerCAmelCase__ ,  return_tensors='''pt''' ,  sampling_rate=lowerCAmelCase__		).input_features.to(self.device		)
							__lowercase					     =				self.speech_model.generate(lowerCAmelCase__ ,  max_length=48_00_00		)

							__lowercase					     =				self.speech_processor.tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__ ,  normalize=lowerCAmelCase__		)[
							    0
							]

							if isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				1
							elif isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
											__lowercase					     =				len(lowerCAmelCase__		)
							else:
											raise ValueError(F"`prompt` has to be of type `str` or `list` but is {type(lowerCAmelCase__		)}"		)

							if height % 8 != 0 or width % 8 != 0:
											raise ValueError(F"`height` and `width` have to be divisible by 8 but are {height} and {width}."		)

							if (callback_steps is None) or (
							    callback_steps is not None and (not isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		) or callback_steps <= 0)
							):
											raise ValueError(
											    F"`callback_steps` has to be a positive integer but is {callback_steps} of type"
											    F" {type(lowerCAmelCase__		)}."		)

							# get prompt text embeddings
							__lowercase					     =				self.tokenizer(
							    lowerCAmelCase__ ,  padding='''max_length''' ,  max_length=self.tokenizer.model_max_length ,  return_tensors='''pt''' ,  )
							__lowercase					     =				text_inputs.input_ids

							if text_input_ids.shape[-1] > self.tokenizer.model_max_length:
											__lowercase					     =				self.tokenizer.batch_decode(text_input_ids[:, self.tokenizer.model_max_length :]		)
											logger.warning(
											    '''The following part of your input was truncated because CLIP can only handle sequences up to'''
											    F" {self.tokenizer.model_max_length} tokens: {removed_text}"		)
											__lowercase					     =				text_input_ids[:, : self.tokenizer.model_max_length]
							__lowercase					     =				self.text_encoder(text_input_ids.to(self.device		)		)[0]

							# duplicate text embeddings for each generation per prompt, using mps friendly method
							__lowercase     ,	__lowercase     ,	__lowercase					     =				text_embeddings.shape
							__lowercase					     =				text_embeddings.repeat(1 ,  lowerCAmelCase__ ,  1		)
							__lowercase					     =				text_embeddings.view(bs_embed * num_images_per_prompt ,  lowerCAmelCase__ ,  -1		)

							# here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
							# of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
							# corresponds to doing no classifier free guidance.
							__lowercase					     =				guidance_scale > 1.0
							# get unconditional embeddings for classifier free guidance
							if do_classifier_free_guidance:
											__lowercase					     =				42
											if negative_prompt is None:
															__lowercase					     =				[''''''] * batch_size
											elif type(lowerCAmelCase__		) is not type(lowerCAmelCase__		):
															raise TypeError(
															    F"`negative_prompt` should be the same type to `prompt`, but got {type(lowerCAmelCase__		)} !="
															    F" {type(lowerCAmelCase__		)}."		)
											elif isinstance(lowerCAmelCase__ ,  lowerCAmelCase__		):
															__lowercase					     =				[negative_prompt]
											elif batch_size != len(lowerCAmelCase__		):
															raise ValueError(
															    F"`negative_prompt`: {negative_prompt} has batch size {len(lowerCAmelCase__		)}, but `prompt`:"
															    F" {prompt} has batch size {batch_size}. Please make sure that passed `negative_prompt` matches"
															    ''' the batch size of `prompt`.'''		)
											else:
															__lowercase					     =				negative_prompt

											__lowercase					     =				text_input_ids.shape[-1]
											__lowercase					     =				self.tokenizer(
											    lowerCAmelCase__ ,  padding='''max_length''' ,  max_length=lowerCAmelCase__ ,  truncation=lowerCAmelCase__ ,  return_tensors='''pt''' ,  )
											__lowercase					     =				self.text_encoder(uncond_input.input_ids.to(self.device		)		)[0]

											# duplicate unconditional embeddings for each generation per prompt, using mps friendly method
											__lowercase					     =				uncond_embeddings.shape[1]
											__lowercase					     =				uncond_embeddings.repeat(1 ,  lowerCAmelCase__ ,  1		)
											__lowercase					     =				uncond_embeddings.view(batch_size * num_images_per_prompt ,  lowerCAmelCase__ ,  -1		)

											# For classifier free guidance, we need to do two forward passes.
											# Here we concatenate the unconditional and text embeddings into a single batch
											# to avoid doing two forward passes
											__lowercase					     =				torch.cat([uncond_embeddings, text_embeddings]		)

							# get the initial random noise unless the user supplied it

							# Unlike in other pipelines, latents need to be generated in the target device
							# for 1-to-1 results reproducibility with the CompVis implementation.
							# However this currently doesn't work in `mps`.
							__lowercase					     =				(batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
							__lowercase					     =				text_embeddings.dtype
							if latents is None:
											if self.device.type == "mps":
															# randn does not exist on mps
															__lowercase					     =				torch.randn(lowerCAmelCase__ ,  generator=lowerCAmelCase__ ,  device='''cpu''' ,  dtype=lowerCAmelCase__		).to(
															    self.device		)
											else:
															__lowercase					     =				torch.randn(lowerCAmelCase__ ,  generator=lowerCAmelCase__ ,  device=self.device ,  dtype=lowerCAmelCase__		)
							else:
											if latents.shape != latents_shape:
															raise ValueError(F"Unexpected latents shape, got {latents.shape}, expected {latents_shape}"		)
											__lowercase					     =				latents.to(self.device		)

							# set timesteps
							self.scheduler.set_timesteps(lowerCAmelCase__		)

							# Some schedulers like PNDM have timesteps as arrays
							# It's more optimized to move all timesteps to correct device beforehand
							__lowercase					     =				self.scheduler.timesteps.to(self.device		)

							# scale the initial noise by the standard deviation required by the scheduler
							__lowercase					     =				latents * self.scheduler.init_noise_sigma

							# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
							# eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
							# eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
							# and should be between [0, 1]
							__lowercase					     =				'''eta''' in set(inspect.signature(self.scheduler.step		).parameters.keys()		)
							__lowercase					     =				{}
							if accepts_eta:
											__lowercase					     =				eta

							for i, t in enumerate(self.progress_bar(lowerCAmelCase__		)		):
											# expand the latents if we are doing classifier free guidance
											__lowercase					     =				torch.cat([latents] * 2		) if do_classifier_free_guidance else latents
											__lowercase					     =				self.scheduler.scale_model_input(lowerCAmelCase__ ,  lowerCAmelCase__		)

											# predict the noise residual
											__lowercase					     =				self.unet(lowerCAmelCase__ ,  lowerCAmelCase__ ,  encoder_hidden_states=lowerCAmelCase__		).sample

											# perform guidance
											if do_classifier_free_guidance:
															__lowercase     ,	__lowercase					     =				noise_pred.chunk(2		)
															__lowercase					     =				noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

											# compute the previous noisy sample x_t -> x_t-1
											__lowercase					     =				self.scheduler.step(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__ ,  **lowerCAmelCase__		).prev_sample

											# call the callback, if provided
											if callback is not None and i % callback_steps == 0:
															callback(lowerCAmelCase__ ,  lowerCAmelCase__ ,  lowerCAmelCase__		)

							__lowercase					     =				1 / 0.1_8215 * latents
							__lowercase					     =				self.vae.decode(lowerCAmelCase__		).sample

							__lowercase					     =				(image / 2 + 0.5).clamp(0 ,  1		)

							# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
							__lowercase					     =				image.cpu().permute(0 ,  2 ,  3 ,  1		).float().numpy()

							if output_type == "pil":
											__lowercase					     =				self.numpy_to_pil(lowerCAmelCase__		)

							if not return_dict:
											return image

							return StableDiffusionPipelineOutput(images=lowerCAmelCase__ ,  nsfw_content_detected=lowerCAmelCase__		)